A Study on the Urban Flood Reservoir by Pareto Optimal Approach

In urban areas, the pavement rate increases significantly; therefore, even under the same rainfall conditions, flooding increases. In addition, when heavy rainfall occurs and drainage system capacity is limited, the watershed tends to be flooded overall, including low-lying areas. Despite many efforts to reduce flooding, the damage continues to recur. As a structural measure to reduce flooding, relatively large reservoirs or pumping stations are installed in upper- and middle-stream areas or at river discharge points. However, due to the characteristics of the urban drainage system, inundation can occur across the watershed, which limits the effectiveness of these measures in reducing overall flooding. This study proposes a distributed installation procedure for small-scale reservoirs, considering economic feasibility and flood reduction, using a Pareto optimization approach that considers flooding characteristics. In this study, the multi-objective optimization technique applied to the Pareto optimization approach is Genetic Algorithms (GA). The objective functions are the minimum cost and maximum flood reduction rate, and the Storm Water Management Model (SWMM) was adopted for runoff simulation. As a result, the size and location of optimal reservoirs representing maximum flood reduction at minimum cost could be selected using the Pareto optimization approach.